{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM example\n",
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To avoid additional dependencies, this uses a very inefficient SVM optimiser!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pylab as plt\n",
    "import simplesvm\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# Generate some data\n",
    "trainx = np.vstack((np.random.randn(50,2),np.random.randn(50,2)+4))\n",
    "traint = np.hstack((-1*np.ones(50,),np.ones(50,)))[:,None]\n",
    "\n",
    "\n",
    "# Uncomment this if you want to add noise (last bit of exercise)\n",
    "for i in range(3):\n",
    "    pos = np.random.randint(50)\n",
    "    if traint[pos] == -1:\n",
    "        traint[pos] = 1\n",
    "    else:\n",
    "        traint[pos] = -1\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1184cf2d0>]"
      ]
     },
     "execution_count": 400,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4aXPO95cAbwM2LF/OPc88k/99bW1s9hgKyr3Xhdoehp2KrNlEfNsXsBr4eoGfVfbjSymf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\nOqDBXCml6oAGc6WUqgP/HwsmhpGwQYumAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118aa6f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "c0 = np.where(traint==-1)[0]\n",
    "c1 = np.where(traint==1)[0]\n",
    "plt.plot(trainx[c0,0],trainx[c0,1],'bo',markersize=10)\n",
    "plt.plot(trainx[c1,0],trainx[c1,1],'ro',markersize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following creates the SVM object. You can vary the kernel type (linear or rbf), the kpar (only makes a difference for rbf) and C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "svm = simplesvm.SimpleSVM(trainx,traint,kernel='rbf',C=1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now call the optimiser to train the SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "svm.smo_optimise()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following block creates a grid of data points and then evaluates the SVM function at each point. We can then draw contours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xvals = np.arange(-3,6,0.1)\n",
    "Ngrid = len(xvals)\n",
    "gridpred = np.zeros((Ngrid,Ngrid))\n",
    "for i in range(len(xvals)):\n",
    "    for j in range(len(xvals)):\n",
    "        pp = np.hstack((xvals[i],xvals[j]))\n",
    "        gridpred[i][j] = svm.test_predict(pp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the data and the contours. The support vectors are highlighted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<a list of 7 text.Text objects>"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UaT2VZoMePbuxOm8voZ8fe+MiVR/JPyOeMGnn6UAKkteDL5eN07mFgEEuMghQ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\nEN+aljtM+3CwpKWlefy7bt6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h/W+3apxU/hpsJIPV/GQpUBhJJJO5u/EMlDBC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\nV1tPYsYLzPhXpZLjVQDto2xv4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N7d/QH1C5fhO4v/1knj3N6FXTwRJashfy2UZTuj03W/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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119a9b190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "c0 = np.where(traint==-1)[0]\n",
    "c1 = np.where(traint==1)[0]\n",
    "sv = np.where(svm.alpha>1e-6)[0]\n",
    "\n",
    "plt.plot(trainx[sv,0],trainx[sv,1],'ko',markersize=20)\n",
    "plt.plot(trainx[c0,0],trainx[c0,1],'bo',markersize=10)\n",
    "plt.plot(trainx[c1,0],trainx[c1,1],'ro',markersize=10)\n",
    "A = plt.contour(xvals,xvals,gridpred.T,linewidths=3)\n",
    "plt.clabel(A, inline=1, fontsize=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the alpha values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   2.49199641e-01]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   3.23502013e-01]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [ -1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   6.50306955e-03]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   7.26673789e-02]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00  -6.93889390e-18]\n",
      " [  1.00000000e+00   3.80482484e-03]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   4.89726381e-01]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]\n",
      " [  1.00000000e+00   0.00000000e+00]]\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "print np.hstack((traint,svm.alpha))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
